Muscle-specific expression of the RNA-binding protein Staufen1 induces progressive skeletal muscle atrophy via regulation of phosphatase tensin homolog
Bibliographic record
Abstract
Converging lines of evidence have now highlighted the key role for post-transcriptional regulation in the neuromuscular system. In particular, several RNA-binding proteins are known to be misregulated in neuromuscular disorders including myotonic dystrophy type 1, spinal muscular atrophy and amyotrophic lateral sclerosis. In this study, we focused on the RNA-binding protein Staufen1, which assumes multiple functions in both skeletal muscle and neurons. Given our previous work that showed a marked increase in Staufen1 expression in various physiological and pathological conditions including denervated muscle, in embryonic and undifferentiated skeletal muscle, in rhabdomyosarcomas as well as in myotonic dystrophy type 1 muscle samples from both mouse models and humans, we investigated the impact of sustained Staufen1 expression in postnatal skeletal muscle. To this end, we generated a skeletal muscle-specific transgenic mouse model using the muscle creatine kinase promoter to drive tissue-specific expression of Staufen1. We report that sustained Staufen1 expression in postnatal skeletal muscle causes a myopathy characterized by significant morphological and functional deficits. These deficits are accompanied by a marked increase in the expression of several atrophy-associated genes and by the negative regulation of PI3K/AKT signaling. We also uncovered that Staufen1 mediates PTEN expression through indirect transcriptional and direct post-transcriptional events thereby providing the first evidence for Staufen1-regulated PTEN expression. Collectively, our data demonstrate that Staufen1 is a novel atrophy-associated gene, and highlight its potential as a biomarker and therapeutic target for neuromuscular disorders and conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".